Submitted by Fangjinhua Wang.

Submission data

Full nameIterMVS-LS
DescriptionWe present IterMVS, a new data-driven method for high-resolution multi-view stereo. We propose a novel GRU-based estimator that encodes pixel-wise probability distributions of depth in its hidden state. Ingesting multi-scale matching information, our model refines these distributions over multiple iterations and infers depth and confidence. To extract the depth maps, we combine traditional classification and regression in a novel manner. We verify the efficiency and effectiveness of our method on DTU, Tanks&Temples and ETH3D. While being the most efficient method in both memory and run-time, our model achieves competitive performance on DTU and better generalization ability on Tanks&Temples as well as ETH3D than most state-of-the-art methods. Code is available at
Parameterstrained on BlendedMVS
Publication titleIterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo
Publication authorsFangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys
Publication URL
Programming language(s)python
Source code or download URL
Submission creation date11 Oct, 2021
Last edited2 May, 2022

High-res multi-view results

multi-view co.statueterrac.

Low-res many-view results

indooroutdoorlakesidesand boxstorage roomstorage room 2tunnel
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Low-res two-view results

Infoalllakes. 1llakes. 1ssand box 1lsand box 1sstora. room 1lstora. room 1sstora. room 2lstora. room 2sstora. room 2 1lstora. room 2 1sstora. room 2 2lstora. room 2 2sstora. room 3lstora. room 3stunnel 1ltunnel 1stunnel 2ltunnel 2stunnel 3ltunnel 3s
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SLAM results

allboxesboxes darkbuddhacables 4cables 5desk 1desk 2desk changing 2desk dark 1desk dark 2desk global light changesdesk ir lightdinodroneforeground occlusionhelmetkidnap 2lamplarge loop 2large loop 3large non loopmotion 2motion 3motion 4planar 1reflective 2scale changetable 1table 2table 5table 6table global light changestable local light changestable scenetrashbin
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